Real-time measurement of oxides of nitrogen from heavy-duty diesel engines

Aravelli, Aparna

doi:10.33915/etd.1364

Cited by 4 publications

(5 citation statements)

References 12 publications

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“…The data used for testing these techniques were collected from the Federal Test Procedure (FTP) cycle on a 1992 Detroit Diesel Corporation (DDC) Series 60 engine and a 2004 Cummins ISM 370 engine and the testing was conducted on a dynamometer in a transient cell. Further details of the test cell and typical operation can be obtained elsewhere [10]. The NO data were collected using a fast NO analyser, which was manufactured by Cambustion Ltd, Cambridge, UK [11,12].…”

Section: Data Used For Analysismentioning

confidence: 99%

Attempts to enhance the differential coefficients method for reconstruction of transient emissions from heavy-duty vehicles

Madireddy

Clark

2008

International Journal of Engine Research

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Engine tailpipe emissions as measured by a sampling system and analyser do not clearly reflect the actual transient emissions from the engine at the tailpipe of the vehicle exhaust. With increasing demand for accurate emissions measurements, several research efforts are being made to compensate for the measurement distortions of the analyser system. The differential coefficients method is one such approach which reconstructs the input signal by approximating the analyser input as a linear combination of the output and the first two derivatives of the output. While the results with this approach were found to be acceptable, this paper focuses on the possibilities of improving the accuracy of the reconstruction by altering the ways in which the numerical derivatives are computed and by considering higher order derivatives in the linear combination. It was found that the use of backward differences in computing the numerical derivatives proved more effective than forward differences. Using higher order derivatives has shown improvement of about 10 per cent over using just the first two derivatives, but this margin may be important for model accuracy or for assessing pass/fail emissions criteria.

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Section: Data Used For Analysismentioning

confidence: 99%

Attempts to enhance the differential coefficients method for reconstruction of transient emissions from heavy-duty vehicles

Madireddy

Clark

2008

International Journal of Engine Research

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“…After determining the C i values, they are normalized, and a curve is generated. This curve is then employed in conjunction with the axle power data to produce the dispersed axle power [57,59].…”

Section: The Sequential Inversion Technique Employs the Dispersion Chmentioning

confidence: 99%

“…The purpose of the dilution tunnel was to dilute raw emissions with ambient air, to simulate the dilution of the tail pipe exhaust in the atmosphere. Constant volume sampling ensures that each analyzer receives the same volume of gas to assess[57]. A schematic of the EERC test setup is shown inFigure 3.4.1[57].…”

mentioning

confidence: 99%

“…Constant volume sampling ensures that each analyzer receives the same volume of gas to assess[57]. A schematic of the EERC test setup is shown inFigure 3.4.1[57].…”

mentioning

confidence: 99%

“…Schematic of Test Setup at EERC[57] Initially data obtained by examining the Federal Test Procedure (FTP) cycle was employed as training data for the emissions module of the ANN. The FTP cycle is made up of four portions, the New York Non Freeway (NYNF), the Los Angeles Non Freeway (LANF), Los Angeles Freeway (LAFY), and the forth portion repeats the NYNF portion.…”

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confidence: 99%

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Development of an Artificial Neural Network to Predict In-Use Engine Emissions

Morris¹

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A method to predict in-use diesel engine emissions is developed based on engine dynamometer and in-use data acquired at the West Virginia University Center for Alternative Fuels, Engines, and Emissions. (WVU CAFEE). The model accounts for the effects of road grade on generated emissions; a need for this model is evident in literature. Current modeling methods do not account for the effects of road grade, and have been shown to under-predict NOx by as much as 57%. It is determined through present research and a review of relevant literature that an artificial neural network (ANN) was the most applicable modeling method. A modular ANN was developed to predict the heavy duty diesel engine emissions. The two modules were trained independently, the first module was trained with data acquired through inuse testing, and the second module was trained with data acquired via engine dynamometer testing. The first module predicted the engine speed and torque associated with the inputs of road grade and vehicle speed, while the second ANN employed the first ANN's outputs, and predicts the emitted quantities of NOx, CO 2 , HC, and CO. A series of training and verification runs are conducted in order to determine the optimum ANN characteristics. Once the ANN was finalized, it was trained with and employed to predict the emissions associated with a variety of routes. When the ANN was trained with a combination of in-use and engine dynamometer data, the ANN is able to predict NOx emissions associated with that same route within 6% of the measured values. The average difference between the measured and predicted CO 2 values for the same training and verification scenario mentioned above was less than 15%. It was also demonstrated that the ANN was able to predict emissions that are associated with routes that differ from those by which it is trained. When the ANN was trained with in-use data from a specific route, it was able to predict the NOx and CO 2 emissions associated with a different route with percent differences from the measured values of 20% or less.

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Methods for reconstruction of transient emissions from heavy -duty vehicles

Madireddy¹

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Methods for Reconstruction of Transient Emissions from Heavy-Duty Vehicles Madhava R. Madireddy Emissions measurement analyzers give out a response that may not reflect the true instantaneous engine-out emissions. Currently, the heavy-duty diesel engines are being certified for emissions measured in a thirty second time window with certain specification requirements for the analyzers. Since these measured emissions values may not be the same as the true instantaneous emissions, integrated values for the thirty second windows may be affected by analyzer response. This document presents and examines reconstruction techniques to estimate instantaneous heavy-duty engine-out emissions. These techniques will take as the input, the continuous set of emissions data and approximate dispersion characteristics of the analyzer employed in measuring the continuous data. For this purpose, this research dealt with understanding and modeling the transient dynamics (dispersion function) of the analyzers and the sampling system to establish a relationship between the measured and instantaneous heavy-duty emissions. Four methods of reconstruction were presented in this study: Sequential Inversion Technique (SIT), Differential Coefficients Method (DCM), Inverse Fast Fourier Transform (IFFT) and Modified Deconvolution Technique (MDT). The application of each method in reconstructing real-time emissions data was presented. While SIT failed in practical applications, each of the other three methods was shown to offer advantage in the post-processing of the measured emissions data. DCM accounted for the small errors

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Real-time measurement of oxides of nitrogen from heavy-duty diesel engines

Cited by 4 publications

References 12 publications

Attempts to enhance the differential coefficients method for reconstruction of transient emissions from heavy-duty vehicles

Attempts to enhance the differential coefficients method for reconstruction of transient emissions from heavy-duty vehicles

Development of an Artificial Neural Network to Predict In-Use Engine Emissions

Methods for reconstruction of transient emissions from heavy -duty vehicles

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